# ------------------------------------------------------------------------------------
# Sparse DETR
# Copyright (c) 2021 KakaoBrain. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------------------
# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# ------------------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------------------


import math

import torch
from torch import nn
from torch.nn.init import xavier_uniform_, constant_, normal_

from .module.ops.modules import MSDeformAttn

from .module.sparse_transformerencoder import DeformableTransformerEncoderLayer, DeformableTransformerEncoder
from .module.sparse_transformerdecoder import DeformableTransformerDecoderLayer, DeformableTransformerDecoder


def build_transformer(args):
    return Transformer(
        d_model=args.hidden_dim,
        nhead=args.nheads,
        num_encoder_layers=args.enc_layers,
        num_decoder_layers=args.dec_layers,
        dim_feedforward=args.dim_feedforward,
        dropout=args.dropout,
        activation="relu",
        return_intermediate_dec=True,
        num_feature_levels=args.num_feature_levels,
        dec_n_points=args.dec_n_points,
        enc_n_points=args.enc_n_points,
        two_stage=args.two_stage,
        two_stage_num_proposals=args.num_queries,
        args=args)



class Transformer(nn.Module):
    def __init__(self, d_model=256, 
                 nhead=8,
                 num_encoder_layers=6, 
                 num_decoder_layers=6, 
                 dim_feedforward=1024, 
                 dropout=0.1,
                 activation="relu", 
                 return_intermediate_dec=False,
                 num_feature_levels=4, 
                 dec_n_points=4,  
                 enc_n_points=4,
                 two_stage=False, 
                 two_stage_num_proposals=300,
                 args=None):
        super().__init__()

        self.d_model = d_model
        self.nhead = nhead
        self.two_stage = two_stage
        self.two_stage_num_proposals = two_stage_num_proposals
        self.eff_query_init = args.eff_query_init
        self.eff_specific_head = args.eff_specific_head
        # there's no need to compute reference points if above 2 conditions meet simultaneously
        self._log_args('eff_query_init', 'eff_specific_head')

########################################################################################################
        self.rho = args.rho
        self.use_enc_aux_loss = args.use_enc_aux_loss
        self.sparse_enc_head = 1 if self.two_stage and self.rho else 0

        if self.rho:
            self.enc_mask_predictor = MaskPredictor(self.d_model, self.d_model)
        else:
            self.enc_mask_predictor = None
########################################################################################################
        self.encoder = self.build_encode(d_model, nhead, num_encoder_layers, dim_feedforward, dropout, activation, num_feature_levels, enc_n_points)
        self.decoder = self.build_decode(d_model, nhead, num_decoder_layers, dim_feedforward, dropout, activation, return_intermediate_dec, num_feature_levels, dec_n_points)
        
        self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))

        if self.two_stage:
            self.enc_output = nn.Linear(d_model, d_model)
            self.enc_output_norm = nn.LayerNorm(d_model)
########################################################################################################
            self.pos_trans = nn.Linear(d_model * 2, d_model * (1 if self.eff_query_init else 2))
            self.pos_trans_norm = nn.LayerNorm(d_model * (1 if self.eff_query_init else 2))
########################################################################################################
            self.reference_points = None
        else:
            self.reference_points = nn.Linear(d_model, 2)

        self._reset_parameters()

    def _log_args(self, *names):
        print('==============')
        print("\n".join([f"{name}: {getattr(self, name)}" for name in names]))
        print('==============')

    def _reset_parameters(self):
        for p in self.parameters():
            if p.dim() > 1:
                nn.init.xavier_uniform_(p)
        for m in self.modules():
            if isinstance(m, MSDeformAttn):
                m._reset_parameters()
        if hasattr(self, 'reference_points') and self.reference_points is not None:
            xavier_uniform_(self.reference_points.weight.data, gain=1.0)
            constant_(self.reference_points.bias.data, 0.)
        normal_(self.level_embed)


    def build_encode(self, d_model, nhead, num_encoder_layers, dim_feedforward, dropout, activation, num_feature_levels, enc_n_points):
        encoder_layer = DeformableTransformerEncoderLayer(d_model, dim_feedforward,
                                                          dropout, activation,
                                                          num_feature_levels, nhead, enc_n_points)
        encoder = DeformableTransformerEncoder(encoder_layer, num_encoder_layers)
        return encoder

    def build_decode(self, d_model, nhead, num_decoder_layers, dim_feedforward, dropout, activation, return_intermediate_dec, num_feature_levels, dec_n_points):
        decoder_layer = DeformableTransformerDecoderLayer(d_model, dim_feedforward,
                                                          dropout, activation,
                                                          num_feature_levels, nhead, dec_n_points)
        decoder = DeformableTransformerDecoder(decoder_layer, num_decoder_layers, return_intermediate_dec)
        return decoder


    def forward(self, srcs, masks, pos_embeds, query_embed=None):
        assert self.two_stage or query_embed is not None

        src, mask, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed, \
        backbone_output_proposals, backbone_topk_proposals, sparse_token_nums, backbone_mask_prediction= \
            self.encode_input_reshape(srcs, masks, pos_embeds)
        # encoder  
        # memory : 1, WH + WH + , 256
        memory, sampling_locations_enc, attn_weights_enc, \
        enc_inter_outputs_class, enc_inter_outputs_coord_unact,  enc_inter_outputs_verb_class, enc_inter_outputs_sub_coord_unact = \
            self.encode_forward(src, mask, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed,  
                                backbone_topk_proposals, sparse_token_nums, backbone_output_proposals, self.encoder)
        # prepare input for decoder
        #   N, 35x35, 256 
        tgt, reference_points, query_embed, init_reference_out, topk_proposals, \
        enc_outputs_class, enc_outputs_coord_unact, enc_outputs_verb_class, enc_outputs_sub_coord_unact = \
            self.decode_input_reshape(query_embed, memory, mask, spatial_shapes, self.reference_points, self.decoder)
        # decoder
        hs, inter_references, sampling_locations_dec, attn_weights_dec = \
            self.decode_forward(tgt, reference_points, memory, spatial_shapes, 
                                level_start_index, valid_ratios,  query_embed, mask, topk_proposals, self.decoder)

        inter_references_out = inter_references

        outputs = {'src':srcs[-1], 'out_query':hs,'memory':memory, 
                   'init_reference':init_reference_out, 'inter_references':inter_references_out} 
        

        outputs.update({'backbone_mask_prediction':backbone_mask_prediction, 
                        'backbone_topk_proposals':backbone_topk_proposals})
        outputs.update({'sampling_locations_enc':sampling_locations_enc,'attn_weights_enc':attn_weights_enc,
                        'sampling_locations_dec':sampling_locations_dec,'attn_weights_dec':attn_weights_dec,
                        'spatial_shapes':spatial_shapes, 'level_start_index':level_start_index})      


        outputs.update({'enc_outputs_class':enc_outputs_class, 
                        'enc_outputs_coord':enc_outputs_coord_unact,
                        'enc_outputs_verb_class':enc_outputs_verb_class,
                        'enc_outputs_sub_coord':enc_outputs_sub_coord_unact})
        
     
        outputs.update({'enc_inter_outputs_class':enc_inter_outputs_class,
                        'enc_inter_outputs_coord':enc_inter_outputs_coord_unact,
                        'enc_inter_outputs_verb_class':enc_inter_outputs_verb_class,
                        'enc_inter_outputs_sub_coord':enc_inter_outputs_sub_coord_unact})      

        

        return outputs


    def encode_input_reshape(self, srcs, masks, pos_embed):
        src_flatten = []
        mask_flatten = []
        lvl_pos_embed_flatten = []
        spatial_shapes = []
        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embed)):
            #   N, 256, 35, 35
            bs, c, h, w = src.shape
            spatial_shape = (h, w)
            spatial_shapes.append(spatial_shape)
            #   N, 35x35, 256
            src = src.flatten(2).transpose(1, 2)
            #   N, 35x35
            mask = mask.flatten(1)
            #   N, 35x35, 256
            pos_embed = pos_embed.flatten(2).transpose(1, 2)
            lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
            lvl_pos_embed_flatten.append(lvl_pos_embed)
            src_flatten.append(src)
            mask_flatten.append(mask) 

        src_flatten = torch.cat(src_flatten, 1)
        mask_flatten = torch.cat(mask_flatten, 1)
        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
        level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)

################################################################################################################ 
        # prepare for sparse encoder
        if self.rho or self.use_enc_aux_loss:
            backbone_output_memory, backbone_output_proposals, valid_token_nums = self.gen_encoder_output_proposals(
                src_flatten+lvl_pos_embed_flatten, mask_flatten, spatial_shapes, process_output=bool(self.rho))
            self.valid_token_nums = valid_token_nums
        else:
            backbone_output_memory = None
            backbone_output_proposals = None
            valid_token_nums = None
                
        if self.rho:
            sparse_token_nums = (valid_token_nums * self.rho).int() + 1
            backbone_topk = int(max(sparse_token_nums))
            self.sparse_token_nums = sparse_token_nums

            backbone_topk = min(backbone_topk, backbone_output_memory.shape[1])

            backbone_mask_prediction = self.enc_mask_predictor(backbone_output_memory).squeeze(-1)
            # excluding pad area
            backbone_mask_prediction = backbone_mask_prediction.masked_fill(mask_flatten, backbone_mask_prediction.min())
            backbone_topk_proposals = torch.topk(backbone_mask_prediction, backbone_topk, dim=1)[1]
        else:
            backbone_topk_proposals = None
            sparse_token_nums= None
            backbone_mask_prediction = None
################################################################################################################ 
        return src_flatten, mask_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, \
            backbone_output_proposals, backbone_topk_proposals, sparse_token_nums, backbone_mask_prediction



    def encode_forward(self, src, mask, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed, 
                        backbone_topk_proposals, sparse_token_nums, backbone_output_proposals, encoder):
        if self.encoder:       
            output_proposals = backbone_output_proposals if self.use_enc_aux_loss else None    
            encoder_output = encoder(src, spatial_shapes, level_start_index, valid_ratios, 
                                    pos=lvl_pos_embed, padding_mask=mask, 
                                    topk_inds=backbone_topk_proposals, output_proposals=output_proposals,
                                    sparse_token_nums=sparse_token_nums)
            
            memory, sampling_locations_enc, attn_weights_enc = encoder_output[:3]

            if self.use_enc_aux_loss:
                enc_inter_outputs_class, enc_inter_outputs_coord_unact = encoder_output[3:5] 
                enc_inter_outputs_verb_class, enc_inter_outputs_sub_coord_unact = encoder_output[5:7] 
            else:
                enc_inter_outputs_class, enc_inter_outputs_coord_unact = None, None    
                enc_inter_outputs_verb_class, enc_inter_outputs_sub_coord_unact = None, None      
        else:
            memory = src + lvl_pos_embed
            sampling_locations_enc, attn_weights_enc = None
            enc_inter_outputs_class, enc_inter_outputs_coord_unact = None, None   
            enc_inter_outputs_verb_class, enc_inter_outputs_sub_coord_unact = None, None
            
            
        return memory, sampling_locations_enc, attn_weights_enc, \
            enc_inter_outputs_class, enc_inter_outputs_coord_unact,  enc_inter_outputs_verb_class, enc_inter_outputs_sub_coord_unact

    def decode_input_reshape(self, query_embed, memory, mask, spatial_shapes, reference_points_func = None, decoder=None):
        bs, _, c = memory.shape
        topk_proposals = None
        
        if self.two_stage:
            #   N, 35x35+, 256
            output_memory, output_proposals, _ = self.gen_encoder_output_proposals(memory, mask, spatial_shapes)
            # hack implementation for two-stage Deformable DETR
        
            enc_outputs_class = decoder.class_embed[decoder.num_layers](output_memory)
            
            enc_outputs_verb_class = decoder.verb_class_embed[decoder.num_layers](output_memory)

            enc_outputs_coord_offset = decoder.bbox_embed[decoder.num_layers](output_memory)
            enc_outputs_coord_unact = output_proposals + enc_outputs_coord_offset  # appendix A.4

            enc_outputs_sub_coord_offset = decoder.sub_bbox_embed[decoder.num_layers](output_memory)
            enc_outputs_sub_coord_unact = output_proposals + enc_outputs_sub_coord_offset
        else:
            enc_outputs_class = None
            enc_outputs_coord_unact = None
            enc_outputs_verb_class = None
            enc_outputs_sub_coord_unact = None        
        #   TODO:
        if self.two_stage:
            topk = self.two_stage_num_proposals    
################################################################################################################ 
            if self.eff_specific_head:
                # 拿出判断对象性的最佳分值，有类的具体头像
                enc_outputs_fg_class = enc_outputs_class.topk(1, dim=2).values[... , 0]
            else:
                # 从二元（前景/背景）类中获取分数，尽管输出有91个输出点，但仅第一点将被用于损失计算
                enc_outputs_fg_class = enc_outputs_class[..., 0]
################################################################################################################  
            topk_proposals = torch.topk(enc_outputs_fg_class, topk, dim=1)[1]
            topk_coords_unact = torch.gather(enc_outputs_coord_unact, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
            topk_coords_unact = topk_coords_unact.detach()
            reference_points = topk_coords_unact.sigmoid()
            init_reference_out = reference_points
            pos_trans_out = self.pos_trans_norm(self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
            if self.eff_query_init:
                # Efficient-DETR uses top-k memory as the initialization of `tgt` (query vectors)
                tgt = torch.gather(memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, memory.size(-1)))
                query_embed = pos_trans_out
            else:
                query_embed, tgt = torch.split(pos_trans_out, c, dim=2)
        else:
            #   100, 256
            if query_embed.dim()==2:
                query_embed, tgt = torch.split(query_embed, c, dim=1)
                query_embed = query_embed.unsqueeze(0).expand(bs, -1, -1)
                tgt = tgt.unsqueeze(0).expand(bs, -1, -1)
            else:#  N, 100, 256
                tgt = torch.zeros_like(query_embed)
            #   学习一个偏移
            reference_points = reference_points_func(query_embed).sigmoid()
            init_reference_out = reference_points
        return tgt, reference_points, query_embed, init_reference_out, topk_proposals, \
            enc_outputs_class, enc_outputs_coord_unact, enc_outputs_verb_class, enc_outputs_sub_coord_unact 



    def decode_forward(self, tgt, reference_points, memory, spatial_shapes, level_start_index, valid_ratios, query_embed, mask, topk_proposals, decoder):
        hs, inter_references, sampling_locations_dec, attn_weights_dec =\
            decoder(tgt, reference_points, memory=memory, src_spatial_shapes=spatial_shapes, 
                    src_level_start_index=level_start_index, src_valid_ratios=valid_ratios, 
                    query_pos=query_embed, src_padding_mask=mask,
                    topk_inds=topk_proposals)
        return hs, inter_references, sampling_locations_dec, attn_weights_dec


    def get_proposal_pos_embed(self, proposals):
        # proposals: N, L(top_k), 4(bbox coords.)
        num_pos_feats = 128
        temperature = 10000
        scale = 2 * math.pi

        dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=proposals.device)  # 128
        dim_t = temperature ** (2 * (dim_t // 2) / num_pos_feats)
        proposals = proposals.sigmoid() * scale  # N, L, 4
        pos = proposals[:, :, :, None] / dim_t  # N, L, 4, 128
        # apply sin/cos alternatively
        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), dim=4)  # N, L, 4, 64, 2
        pos = pos.flatten(2)  # N, L, 512 (4 x 128)
        return pos

    def gen_encoder_output_proposals(self, memory, mask, spatial_shapes, process_output=True):
        """Make region proposals for each multi-scale features considering their shapes and padding masks, 
        and project & normalize the encoder outputs corresponding to these proposals.
            - center points: relative grid coordinates in the range of [0.01, 0.99] (additional mask)
            - width/height:  2^(layer_id) * s (s=0.05) / see the appendix A.4
        
        Tensor shape example:
            Args:
                memory: torch.Size([2, 15060, 256])
                memory_padding_mask: torch.Size([2, 15060])
                spatial_shape: torch.Size([4, 2])
            Returns:
                output_memory: torch.Size([2, 15060, 256])
                    - same shape with memory ( + additional mask + linear layer + layer norm )
                output_proposals: torch.Size([2, 15060, 4]) 
                    - x, y, w, h
        """
        N_, S_, C_ = memory.shape
        proposals = []
        _cur = 0
        for lvl, (H_, W_) in enumerate(spatial_shapes):
            # level of encoded feature scale
            mask_flatten_ = mask[:, _cur:(_cur + H_ * W_)].view(N_, H_, W_, 1)
            valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
            valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)

            grid_y, grid_x = torch.meshgrid(torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
                                            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device))
            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)

            scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
            grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
            wh = torch.ones_like(grid) * 0.05 * (2.0 ** lvl)
            proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
            proposals.append(proposal)
            _cur += (H_ * W_)
            
        output_proposals = torch.cat(proposals, 1)
        output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(-1, keepdim=True)  
        output_proposals = torch.log(output_proposals / (1 - output_proposals))  # inverse of sigmoid
        output_proposals = output_proposals.masked_fill(mask.unsqueeze(-1), float('inf')) 
        output_proposals = output_proposals.masked_fill(~output_proposals_valid, float('inf'))  # sigmoid(inf) = 1

        output_memory = memory
        
        if process_output:
            output_memory = output_memory.masked_fill(mask.unsqueeze(-1), float(0))
            output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
            output_memory = self.enc_output_norm(self.enc_output(output_memory))
        
        return output_memory, output_proposals, (~mask).sum(axis=-1)

    def get_valid_ratio(self, mask):
        _, H, W = mask.shape
        valid_H = torch.sum(~mask[:, :, 0], 1)
        valid_W = torch.sum(~mask[:, 0, :], 1)
        valid_ratio_h = valid_H.float() / H
        valid_ratio_w = valid_W.float() / W
        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
        return valid_ratio


class MaskPredictor(nn.Module):
    def __init__(self, in_dim, h_dim):
        super().__init__()
        self.h_dim = h_dim
        self.layer1 = nn.Sequential(
            nn.LayerNorm(in_dim),
            nn.Linear(in_dim, h_dim),
            nn.GELU()
        )
        self.layer2 = nn.Sequential(
            nn.Linear(h_dim, h_dim // 2),
            nn.GELU(),
            nn.Linear(h_dim // 2, h_dim // 4),
            nn.GELU(),
            nn.Linear(h_dim // 4, 1)
        )
    
    def forward(self, x):
        z = self.layer1(x)
        z_local, z_global = torch.split(z, self.h_dim // 2, dim=-1)
        z_global = z_global.mean(dim=1, keepdim=True).expand(-1, z_local.shape[1], -1)
        z = torch.cat([z_local, z_global], dim=-1)
        out = self.layer2(z)
        return out
    

